Atezolizumab and Bevacizumab Combination Therapy in the Treatment of Advanced Hepatocellular Cancer

Liver cancer, particularly hepatocellular carcinoma, is a global concern. This study focuses on the evaluation of Atezolizumab and Bevacizumab combination therapy as a promising alternative in the treatment of advanced hepatocellular carcinoma. The objectives of this systematic review include evaluating the efficacy of Atezolizumab and Bevacizumab combination therapy compared to conventional therapies with Sorafenib and other conventional therapies, analyzing the associated adverse effects, and exploring prognostic factors in the setting of advanced hepatocellular carcinoma. A systematic literature review was carried out using the PubMed and Web of Science databases. Fifteen related articles were included and evaluated according to their level of evidence and recommendation. Results: The combination therapy of Atezolizumab and Bevacizumab, along with Sorafenib, showed positive results in the treatment of patients with advanced hepatocellular carcinoma. Significant adverse effects were identified, such as gastrointestinal bleeding, arterial hypertension, and proteinuria, which require careful attention. In addition, prognostic factors, such as transforming growth factor beta (TGF-β), alpha-fetoprotein (AFP), and vascular invasion, were highlighted as key indicators of hepatocellular carcinoma progression. Conclusions: The combination of Atezolizumab and Bevacizumab is shown to be effective in the treatment of advanced hepatocellular carcinoma, although it is essential to take into consideration the associated adverse effects. The prognostic factors identified may provide valuable information for the clinical management of this disease. This study provides a comprehensive overview of a promising emerging therapy for liver cancer.

Echocardiographic phenotyping of the continuum of myocardial functional remodelling in left ventricular hypertrophy - machine learning validated with cardiac magnetic resonance

Funding Acknowledgements

Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Horizon 2020 European Commission Project H2020-MSCA-ITN-2016 (764738) and the Clinical Research in Cardiology grant from the Spanish Cardiac Society

Background

Exploring phenotypes of left ventricular hypertrophy (LVH) and interpreting the relationship of genotype and phenotype are contemporary clinical challenges. Machine learning (ML) can help by integrating whole-cardiac cycle echo data and separating patients based on subtle differences of cardiac function. The aim is to investigate if an unsupervised ML approach has the potential to explore the LVH spectrum and recognize phenotypes related to distinct disease aetiologies and genotypes.

Methods

The cohort consisted of 342 participants: patients with hypertrophic cardiomyopathy (HCM)(n = 27), HCM relatives (n = 31), hypertensive patients (HTN) (n = 189), and healthy individuals (n = 95). All had echocardiography performed, whereas magnetic resonance (MR) and genetic testing were performed when clinically indicated. Myocardial deformation of the LV and left atrium, aortic and mitral blood-pool Doppler, as well as the septal mitral annular tissue Doppler velocity profiles were used as input for ML. Clinical data, including echo measurements, were not part of the learning, but used to validate the ML-derived phenotypes. An unsupervised ML algorithm was used to create an output space where participants were positioned based on cardiac function. Regression was used to estimate the echo and clinical characteristics of different regions in the space. 

Results

The ML analysis of HCM and relative data shows grouping of HCM patients in the right-most region of the output space (Fig 1B). This region was related to LV outflow tract obstruction, mitral inflow fusion, systolic impairment with septal involvement, as well as LA and LV strain impairment (Fig 1A). Clinical data concurred - showing reduced global longitudinal strain, elevated LV mass, and a pattern of systolic and diastolic impairment - defining a comprehensive phenotype of LV remodelling related to HCM. Exploration of the genotype/phenotype relationship revealed G + P- relatives grouping on the transition from the healthy to the remodelling region. Projection of the HTN and healthy individuals into the HCM space defined the LVH disease spectrum, with healthy individuals projecting in the existing healthy region and HTNs in the transition from health to extreme remodelling (Fig 1C). MR findings of late gadolinium enhancement correlated with the ML-derived functional remodelling phenotype (Fig 1C). Furthermore, 6 patients with a clinical need for septal myectomy were located in the extreme remodelling part of the output space (Fig 1C, red circles).

Conclusion

ML can integrate complex, whole-cardiac cycle echo data to group patients based on similarity of cardiac function. Using an interpretable ML approach, we can explore the spectrum of LV remodelling in different aetiologies and interpret the relationship between genotype and phenotype. The methodology can accommodate new patients by projecting them into the existing space to aid in clinical interpretation, risk assessment and patient management.

Abstract Figure 1

Aetiology-discriminative multimodality imaging of hypertrophic cardiomyopathy: deformation patterns relate to synchrotron-based assessment of microstructural tissue remodelling

Funding Acknowledgements

Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Horizon 2020 European Commission Project H2020-MSCA-ITN-2016 (764738) and the Clinical Research in Cardiology grant from the Spanish Cardiac Society.

Background

The aetiology of left ventricular hypertrophy (LVH) is a relevant clinical challenge with consequences for patient management. Phenotypes resulting from hypertensive remodelling and sarcomere mutation often overlap. Synchrotron X-ray phase-contrast imaging (X-PCI) is a technique that can provide 3-dimensional detailed information on myocardial micro-structure non-destructively. The aim is to relate macrostructural/functional, non-invasive, imaging phenotypes of hypertrophic cardiomyopathy (HCM) to the underlying myocardial microstructure assessed with X-PCI.

Methods

Myocardial tissue samples were obtained from three patients (P1-3) with obstructive myocardial hypertrophy undergoing septal myectomy. Medical history and the 5-year HCM risk scores were evaluated. The patients were imaged with magnetic resonance imaging and echocardiography prior to procedure. Myocardial structure was assessed with wall thickness, late gadolinium enhancement (LGE), whereas function with speckle-tracking deformation (STE) and tissue Doppler imaging (TDI). Myectomy tissue was imaged with X-PCI in the TOMCAT beamline, using a multiscale propagation-based protocol combining a low-resolution (LR) and a high-resolution (HR) setup (5.8 and 0.7 um pixel size, respectively).

Results

The clinical and imaging data are shown in Fig 1. On initial assessment, wall thickness, LGE distribution, global longitudinal strain and septal TDI demonstrated a similar macrostructural and functional phenotype of P1 and P2, whereas P3 stood out with more severe hypertrophy, scarring and dysfunction. Additional regional deformation analysis with STE revealed reduced deformation in the basal and mid septum in P1, paired with a hypertensive pattern of post-systolic shortening (PSS) (yellow arrows). In comparison, in P2 and P3, deformation was more heterogeneous regionally, with regions of almost complete absence of deformation (orange arrows). Upon further exploration with TDI, areas with abnormal deformation were identified on the transition from basal to mid septum in both P2 and P3, whereas deformation was normal, but reduced in P1, and paired with PSS. LR X-PCI defined regions of interest to scan with HR (yellow frame), where HR revealed extensive interstitial fibrosis (orange arrow) with normal myocyte size and organisation in P1, compatible with severe hypertensive remodelling. However, in P2 and P3, patches of fibrosis (yellow arrow) paired with enlarged myocytes organized in visible disarray, considerably more prominent in P3, were both compatible with sarcomere-mutation HCM.

Conclusion

The results demonstrate multiscale phenotyping of HCM - relating micro- and macrostructural findings to function, and integrating multimodality data. In-depth regional deformation analysis, validated by synchrotron-based microstructural analysis, showed potential to identify distinct imaging phenotypes in HCM, distinguishing between overlapping presentations in different aetiologies.

Abstract Figure 1